code: def _load_chunk(dat_path, cat_path, info_path):
      """Loads a data chunk as specified by the paths.
  
      Args:
        dat_path: Path to dat file of the chunk.
        cat_path: Path to cat file of the chunk.
        info_path: Path to info file of the chunk.
  
      Returns:
        Tuple with the dat, cat, info_arrays.
      """
      dat_array = read_binary_matrix(dat_path)
      # Even if the image is gray scale, we need to add an extra channel dimension
      # to be compatible with tfds.features.Image.
      dat_array = np.expand_dims(dat_array, -1)
  
      cat_array = read_binary_matrix(cat_path)
  
      info_array = read_binary_matrix(info_path)
      info_array = np.copy(
          info_array
      )  # Make read-only buffer array writable.
      # Azimuth values are 0, 2, 4, .., 34. We divide by 2 to get proper labels.
      info_array[:, 2] = info_array[:, 2] / 2
  
      return dat_array, cat_array, info_array
  
file_path: datasets/tensorflow_datasets/datasets/smallnorb/smallnorb_dataset_builder.py
imports: import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling a global function with example usage: _load_chunk(dat_path, cat_path, info_path) and returns: dat_array,  cat_array,  info_array